Vision

To bring forth technically versatile, Research oriented, Industry ready engineers in the field of Artificial Intelligence and Machine Learning.

Mission

• Facilitate modern infrastructure and versatile learning resources to produce self-sustainable professionals

•  Facilitate project based learning and skill upgradation through industry  collaborations

•  Inculcate professional ethics, leadership qualities and practice lifelong learning

• PSO1: Graduates will have the ability to adapt, contribute and innovate ideas in the field of Artificial Intelligence and Machine Learning.

• PSO2: To provide a concrete foundation and enrich their abilities to qualify for Employment, Higher studies and Research in various domains of Artificial Intelligence and Machine Learning such as Data Science, Computer Vision, Natural Language Processing with Ethical Values.

• PSO3: Graduates will acquire the practical proficiency with niche technologies and open-source platforms and to become Entrepreneur in the domain Artificial Intelligence and Machine Learning.

• PEO1: Attain proficiency in professional practice

• PEO2: Practice technical skills to identify, analyze and solve complex problems related to Artificial Intelligence and Machine Learning.

• PEO3: Emerge as an Individual or a team member with societal concerns, ethics and motivated for holistic learning.

Module 1

INTRODUCTION TO DATA STRUCTURES: Data Structures, Classifications (Primitive & Non-Primitive), Data structure Operations Review of pointers and dynamic Memory Allocation,

ARRAYS and STRUCTURES: Arrays, Dynamic Allocated Arrays, Structures and Unions, Polynomials, Sparse Matrices, representation of Multidimensional Arrays, Strings

STACKS: Stacks, Stacks Using Dynamic Arrays, Evaluation and conversion of Expressions

Module 2

QUEUES: Queues, Circular Queues, Using Dynamic Arrays, Multiple Stacks and queues.

LINKED LISTS : Singly Linked, Lists and Chains, Representing Chains in C, Linked Stacks and Queues, Polynomials

Module 3

LINKED LISTS : Additional List Operations, Sparse Matrices, Doubly Linked List.

TREES: Introduction, Binary Trees, Binary Tree Traversals, Threaded Binary Trees.

Module 4

TREES(Cont..): Binary Search trees, Selection Trees, Forests, Representation of Disjoint  sets, Counting Binary Trees,

GRAPHS: The Graph Abstract Data Types, Elementary Graph Operations

Module 5

HASHING: Introduction, Static Hashing, Dynamic Hashing

PRIORITY QUEUES: Single and double ended Priority Queues, Leftist Trees

INTRODUCTION TO EFFICIENT BINARY SEARCH TREES: Optimal Binary Search Trees